Adaptive AI-Assisted Model Predictive Current Control for PMSM Drives in Electric Vehicles with Robustness Enhancement and Real-Time Feasibility Analysis

Adaptive AI-Assisted Model Predictive Current Control for PMSM Drives in Electric Vehicles with Robustness Enhancement and Real-Time Feasibility Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Adaptive AI-Assisted Model Predictive Current Control for PMSM Drives in Electric Vehicles with Robustness Enhancement and Real-Time Feasibility Analysis Vo Thanh Ha, Nguyen Hai, Nguyen Tam Thanh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9159305/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract In this paper, we propose an adaptive AI-assisted model predictive current control (AI-MPC) strategy for permanent magnet synchronous motor (PMSM) drivthe es in electric vehicles (EVs). Whereas classic finite control set model predictive current control (FCS-MPCC) relies on a predetermined set of weighting factors, a neural network-based solution is proposed to enable adaptability of cost function weights online. The prediction is done with a discrete-time PMSM model combined with vehicle load dynamics, while Lyapunov-based analysis ensur conducted toonline adaptation of cost function weightstem in the presence ofsence of disturbance speed variation, the simulation results presented under various EV operating scenarios, including overshoot by 40%, torque ripple by 35%, and RMSE by 30% versus FCS-MPCC. This also achieves a much faster settling time (≈ 3 ms) and increased robustness. These findings demonstrate that the predictive control framework proposed using AI is a promising solution for real-time high-performance EV motor drives. Electric vehicles PMSM drives Model predictive control AI-assisted control Neural Network PMSM Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 05 May, 2026 Reviews received at journal 03 May, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviews received at journal 27 Apr, 2026 Reviewers agreed at journal 18 Apr, 2026 Reviews received at journal 13 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers invited by journal 29 Mar, 2026 Editor assigned by journal 24 Mar, 2026 Submission checks completed at journal 24 Mar, 2026 First submitted to journal 18 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9159305","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":613994405,"identity":"a0be498e-033e-4586-88bc-9fd92d4d8853","order_by":0,"name":"Vo Thanh Ha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYHACNiA+kACmeSogQhL4dTDDtTA28JyBqCZBC28bEVoMjvcfe/Bzx508/ga29Adv5x2u429gPnibh+FwHk4tZw6zG/aeeVYscYDtYOPcbYclgIxka6CWYlxaJGcks0nwth1ObDjA3tjMC9RiwMBjJs3DkJbYgEeL5F+glvlgLXNAWvi/4dXCL5HMJg2yZQPQYc28DWBb2IBabHBr4TlsJi0L1LLxMFvizDnH0iVnHGYztpxjgFsLG3vjM8m3QC3zjrcZfHhTY83P39788MabCgmcWhCAGYVhQFD9KBgFo2AUjAI8AABPvFVSLUtvqgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Transport and Communications","correspondingAuthor":true,"prefix":"","firstName":"Vo","middleName":"Thanh","lastName":"Ha","suffix":""},{"id":613994406,"identity":"c37b33bf-04f9-4393-a14f-d430fc87aa16","order_by":1,"name":"Nguyen Hai","email":"","orcid":"","institution":"University of Transport and Communications","correspondingAuthor":false,"prefix":"","firstName":"Nguyen","middleName":"","lastName":"Hai","suffix":""},{"id":613994407,"identity":"e020b1f0-b79e-4408-a215-bf8a7b31451c","order_by":2,"name":"Nguyen Tam Thanh","email":"","orcid":"","institution":"Vietnam Maritime University","correspondingAuthor":false,"prefix":"","firstName":"Nguyen","middleName":"Tam","lastName":"Thanh","suffix":""}],"badges":[],"createdAt":"2026-03-18 12:23:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9159305/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9159305/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106093675,"identity":"5de834a2-4d6c-4ac2-8a96-030b9a27c695","added_by":"auto","created_at":"2026-04-03 11:38:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1630379,"visible":true,"origin":"","legend":"","description":"","filename":"ArticleTitle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9159305/v1_covered_084c844b-3068-4442-860d-c376733bf888.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Adaptive AI-Assisted Model Predictive Current Control for PMSM Drives in Electric Vehicles with Robustness Enhancement and Real-Time Feasibility Analysis","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Electric vehicles, PMSM drives, Model predictive control, AI-assisted control, Neural Network, PMSM","lastPublishedDoi":"10.21203/rs.3.rs-9159305/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9159305/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this paper, we propose an adaptive AI-assisted model predictive current control (AI-MPC) strategy for permanent magnet synchronous motor (PMSM) drivthe es in electric vehicles (EVs). Whereas classic finite control set model predictive current control (FCS-MPCC) relies on a predetermined set of weighting factors, a neural network-based solution is proposed to enable adaptability of cost function weights online. The prediction is done with a discrete-time PMSM model combined with vehicle load dynamics, while Lyapunov-based analysis ensur conducted toonline adaptation of cost function weightstem in the presence ofsence of disturbance speed variation, the simulation results presented under various EV operating scenarios, including overshoot by 40%, torque ripple by 35%, and RMSE by 30% versus FCS-MPCC. This also achieves a much faster settling time (≈ 3 ms) and increased robustness. These findings demonstrate that the predictive control framework proposed using AI is a promising solution for real-time high-performance EV motor drives.\u003c/p\u003e","manuscriptTitle":"Adaptive AI-Assisted Model Predictive Current Control for PMSM Drives in Electric Vehicles with Robustness Enhancement and Real-Time Feasibility Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-02 02:34:10","doi":"10.21203/rs.3.rs-9159305/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-05T17:53:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T08:51:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112319960595355385031452728993006172302","date":"2026-04-30T15:55:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T04:45:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140309173787647954787293136519217327579","date":"2026-04-19T03:29:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-14T03:56:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"142419040316524760509097638521714624229","date":"2026-04-13T10:03:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-29T10:14:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-24T12:02:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-24T12:01:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Applied Sciences","date":"2026-03-18T12:08:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"809fd8a9-a093-407d-8870-6291a76b5862","owner":[],"postedDate":"April 2nd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-05T17:53:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T08:51:40+00:00","index":30,"fulltext":""},{"type":"reviewerAgreed","content":"112319960595355385031452728993006172302","date":"2026-04-30T15:55:28+00:00","index":29,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T05:10:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-02 02:34:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9159305","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9159305","identity":"rs-9159305","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}